Tire wear estimation system, tire wear estimation method, and calculation model generation system

Abstract

Provided is a technique capable of improving the estimation accuracy of the wear state of a tire. 【Solution means】The tire wear estimation system 100 includes a vehicle information acquisition unit 12, a groove information acquisition unit 13, and a wear estimation unit 15. The vehicle information acquisition unit 12 acquires information including the driving situation of the vehicle. The groove information acquisition unit 13 acquires the new groove depth, which is the groove depth of the tire 7 mounted on the vehicle in a new state, and the starting groove depth, which is the groove depth at the time point that is the starting point of the wear estimation of the tire 7. The wear estimation unit 15 estimates the wear state of the tire 7 based on the driving situation acquired by the vehicle information acquisition unit 12 and the new groove depth and the starting groove depth acquired by the groove information acquisition unit 13, using the machine-learned arithmetic model 15a as input data.

Description

Technical Field

[0001] The present invention relates to a tire wear estimation system, a tire wear estimation method, and a calculation model generation system for estimating the wear state of tires mounted on vehicles.

Background Art

[0002] Generally, tires wear as the driving state, driving distance, etc. change. Recently, the development of a technology for estimating the tire wear amount using a calculation model with information measured by a vehicle as input data has been underway.

[0003] Patent Document 1 describes a conventional generation system for a calculation model for estimating the tire wear amount. In the conventional calculation model generation system, a tire information acquisition unit acquires tire data including the temperature and pressure of the tire. A position information acquisition unit acquires position data of the vehicle on which the tire is mounted. A wear amount calculation unit has a calculation model for calculating the tire wear amount based on the temperature, pressure, and position, and inputs the tire data and the position data corresponding to the tire data to calculate the tire wear amount using the calculation model. A calculation model update unit compares the wear amount measured by the tire with the wear amount calculated by the wear amount calculation unit, and updates the calculation model.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] When constructing a machine learning-based arithmetic model with, for example, the wear state of each groove of a tire as the target variable and parameters including at least the driving conditions of a vehicle as the explanatory variables, the inventor considered that there is room for improvement in improving the estimation accuracy of the wear state of the tire by appropriately providing the explanatory variables. Further, the inventor considered that it is necessary to devise the setting of the explanatory variables in view of the fact that tires during use may be removed and attached due to summer / winter tire replacement or tire rotation.

[0006] The present invention has been made in view of such circumstances, and an object thereof is to provide a tire wear estimation system, a tire wear estimation method, and an arithmetic model generation system capable of improving the estimation accuracy of the wear state of a tire.

Means for Solving the Problems

[0007] A tire wear estimation system according to an aspect of the present invention includes a vehicle information acquisition unit that acquires information including driving conditions of a vehicle, a new groove depth that is a groove depth of a tire mounted on the vehicle in a new state, and a starting groove depth that is a groove depth at a point in time that is a starting point of wear estimation of the tire, and a groove information acquisition unit that acquires a wear estimation unit that estimates a wear state of the tire based on a machine-learned arithmetic model using the driving conditions acquired by the vehicle information acquisition unit, and the new groove depth and the starting groove depth acquired by the groove information acquisition unit as input data.

[0008] Another aspect of the present invention is a tire wear estimation method. The tire wear estimation method includes a vehicle information acquisition step of acquiring information including driving conditions of a vehicle, a groove information acquisition step of acquiring a new groove depth that is a groove depth of a tire mounted on the vehicle in a new state, and a starting groove depth that is a groove depth at a point in time that is a starting point of wear estimation of the tire, and a wear estimation step of estimating a wear state of the tire based on a machine-learned arithmetic model using the driving conditions acquired by the vehicle information acquisition step, and the new groove depth and the starting groove depth acquired by the groove information acquisition step as input data.

[0009] Another aspect of the present invention is an arithmetic model generation system. The arithmetic model generation system includes a vehicle information acquisition unit that acquires information including the driving situation of a vehicle, a new groove depth that is the groove depth of a tire mounted on the vehicle in a new state, and a starting groove depth that is the groove depth at the time point that is the starting point of wear estimation of the tire. A groove information acquisition unit that acquires the starting groove depth, a wear estimation unit that estimates the wear state of the tire based on a learning-type arithmetic model using the driving situation acquired by the vehicle information acquisition unit, the new groove depth, and the starting groove depth acquired by the groove information acquisition unit as input data, and a learning processing unit that learns the arithmetic model using the measured wear state of the tire as teacher data.

Effects of the Invention

[0010] According to the present invention, the estimation accuracy of the wear state of a tire can be improved.

Brief Description of the Drawings

[0011]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Modes for Carrying Out the Invention

[0012] Hereinafter, the present invention will be described with reference to FIGS. 1 to 8 based on preferred embodiments. The same or equivalent components and members shown in each drawing are denoted by the same reference numerals, and redundant explanations will be omitted as appropriate. Also, the dimensions of the members in each drawing are shown enlarged or reduced as appropriate for easy understanding. In addition, some of the members that are not important for explaining the embodiments in each drawing are omitted from the display.

[0013] (Embodiment) FIG. 1 is a block diagram showing the functional configuration of a tire wear estimation system 100 according to an embodiment. The tire wear estimation system 100 includes an in-vehicle measurement device 70 mounted on a vehicle, a weather information server device 80, and a wear estimation device 10 that estimates the wear state of each tire 7 mounted on the vehicle.

[0014] The wear estimation device 10 acquires vehicle measurement information such as the speed, acceleration, and position information of the vehicle from the in-vehicle measurement device 70 mounted on the vehicle via a communication network 9 such as the Internet. The wear estimation device 10 also acquires weather information from the weather information server device 80. Further, the wear estimation device 10 acquires information on the groove depth of the tire 7 in a new state (hereinafter referred to as "new groove depth"), and the groove depth at the time point that serves as the starting point for estimating the wear state of the tire 7 (hereinafter referred to as "starting groove depth"). The wear estimation device 10 performs calculations using a learning-based calculation model 15a based on the acquired information to estimate the wear state of each tire 7.

[0015] The wear state of the tire 7 estimated by the wear estimation device 10 is represented by information such as the wear amount and wear rate of the tire 7. The wear state of the tire 7 may be, for example, the amount of wear (a value such as 1 mm), or the ratio of the amount of wear to the initial groove depth in the tire groove (a value such as 10%). Wear estimation means estimating the wear state of the tire 7, that is, information such as the wear amount and wear rate of the tire 7.

[0016] FIG. 2 is a block diagram showing the functional configuration of the in-vehicle measurement device 70. The in-vehicle measurement device 70 includes a vehicle measurement unit 71, a tire measurement unit 72, an information acquisition unit 73, and a communication unit 74. Each unit in the in-vehicle measurement device 70 can be realized in terms of hardware by electronic elements such as a computer's CPU and mechanical parts, and can be realized in terms of software by a computer program or the like. Here, however, functional blocks realized by their cooperation are depicted. Therefore, it is understood by those skilled in the art that these functional blocks can be realized in various forms by combinations of hardware and software.

[0017] The vehicle measurement unit 71 has a speedometer 71a, a GPS receiver 71b, and an acceleration sensor 71c mounted on the vehicle, and measures the driving status of the vehicle. The speedometer 71a measures the driving speed of the vehicle. The GPS receiver 71b measures the current position information (latitude, longitude, and altitude) of the vehicle. The acceleration sensor 71c measures the acceleration in the three-axis directions of the vehicle. The three-axis directions are, for example, the front-rear direction, the left-right direction, and the up-down direction of the vehicle.

[0018] The tire measurement unit 72 has a temperature sensor 72a and a pressure sensor 72b. The temperature sensor 72a and the pressure sensor 72b are disposed at the air valve or the like of the tire 7 mounted on the vehicle, or are firmly wound around and fixed to the wheel with a belt or the like, and measure the temperature and air pressure of the tire 7. The temperature sensor 72a may be disposed on the inner liner or the like of the tire 7. Incidentally, the acceleration sensor 71c may be disposed on the inner liner of the tire 7.

[0019] The information acquisition unit 73 acquires vehicle measurement information (such as traveling speed, position information, acceleration, etc.) measured by the vehicle measurement unit 71, tire measurement information (such as tire temperature and air pressure, etc.) measured by the tire measurement unit 72, and tire identification information and the like described later. The information acquisition unit 73 associates the measured time information or the acquired time information with each measurement data included in the vehicle measurement information and the tire measurement information. The information acquisition unit 73 transmits the vehicle measurement information and the tire measurement information together with the time information associated with each measurement data from the communication unit 74 to the wear estimation device 10.

[0020] When an electronic control device of the vehicle or a device such as a digital tachometer is installed in the vehicle, the information acquisition unit 73 may acquire the traveling speed, acceleration, position information, etc. of the vehicle collected by the device. The communication unit 74 is communicatively connected to the communication network 9 by wireless communication such as WiFi (registered trademark), and transmits the vehicle measurement information, tire measurement information, and time information acquired by the information acquisition unit 73 to the wear estimation device 10 via the communication network 9.

[0021] Returning to FIG. 1, the weather information server device 80 provides weather information in various places. The weather information provided by the weather information server device 80 is information including precipitation, snow accumulation, snowfall, temperature, sunshine duration, etc. in various places. The wear estimation device 10 acquires the weather information at the location where the vehicle is traveling from the weather information server device 80.

[0022] The wear estimation device 10 includes a communication unit 11, a vehicle information acquisition unit 12, a groove information acquisition unit 13, a groove information management unit 14, a wear estimation unit 15, and a storage unit 16. Each part in the wear estimation device 10 can be realized by electronic elements such as a computer CPU and mechanical parts in terms of hardware, and can be realized by a computer program or the like in terms of software. Here, however, functional blocks realized by their cooperation are depicted. Therefore, it is understood by those skilled in the art that these functional blocks can be realized in various forms by a combination of hardware and software.

[0023] The communication unit 11 is communicatively connected to the communication network 9 by wireless or wired communication and communicates with the communication unit 74 of the in-vehicle measurement device 70. The communication unit 11 also communicates with the weather information server device 80 via the communication network 9.

[0024] The vehicle information acquisition unit 12 acquires vehicle measurement information (travel speed, position information, acceleration, etc.) and tire measurement information (tire temperature, air pressure, etc.) transmitted from the in-vehicle measurement device 70 mounted on the vehicle. The vehicle information acquisition unit 12 stores the acquired vehicle measurement information in the storage unit 16 as vehicle measurement data 16a. The vehicle measurement data 16a is used to calculate the driving conditions such as the driving distance of the vehicle used for estimating the wear of the tire 7. Also, when the vehicle information acquisition unit 12 uses the tire measurement information for estimating the wear of the tire 7, the acquired tire measurement information is stored in the storage unit 16.

[0025] The vehicle information acquisition unit 12 can read the vehicle measurement data 16a from the storage unit 16 and calculate and acquire the driving distance based on the position information. The vehicle information acquisition unit 12 uses the first date and time information D1, the second date and time information D2, and the third date and time information D3, which will be described later. The driving distance of the vehicle may be calculated based on the driving speed data in the vehicle measurement data 16a and the data of the time associated with the data. That is, the driving distance of the vehicle can be calculated by multiplying the speed data arranged in time series by the time difference until the next time point. The driving speed of the vehicle may use the one calculated from the driving distance of the vehicle based on the position information arranged in time series and the acquisition interval of the position information.

[0026] If information regarding the driving distance of the vehicle is provided from the vehicle or an external device for vehicle management, etc., the vehicle information acquisition unit 12 does not need to calculate the driving distance by itself and may acquire information regarding the driving distance from the vehicle or the external device.

[0027] The vehicle information acquisition unit 12 outputs the acquired driving distance to the wear estimation unit 15. When the tire measurement information (such as the temperature and air pressure of the tire) is used for estimating the wear of the tire 7, the vehicle information acquisition unit 12 outputs the acquired tire measurement information to the wear estimation unit 15. The vehicle information acquisition unit 12 may output the information on speed and acceleration in the vehicle measurement data 16a to the wear estimation unit 15.

[0028] In addition, the vehicle information acquisition unit 12 acquires, from the storage unit 16, the data used for estimating the wear state of the tire 7 among the vehicle specification data 16c, the tire specification data 16d, and the tire position data 16e, and outputs it to the wear estimation unit 15. The storage unit 16 is a storage device composed of, for example, an SSD (Solid State Drive), a hard disk, a CD-ROM, a DVD, etc., and stores data provided in advance regarding the specifications of various vehicles and the tire 7.

[0029] The vehicle specification data 16c includes information regarding the performance of the vehicle, such as the manufacturer, vehicle name, vehicle model, vehicle body weight, drive train, overall length, vehicle width, vehicle height, maximum load capacity, etc. In addition, the tire specification data 16d includes information regarding the performance of the tire 7, such as the manufacturer, product name, tire size, tire width, aspect ratio, wear resistance performance, tire strength, static rigidity, dynamic rigidity, tire outer diameter, load index, manufacturing date, etc. The tire position data 16e includes the mounting position of the tire 7 to be worn on the vehicle, tire identification information, and information regarding the axle to which it is attached. The tire identification information is a series of numbers, such as a manufacturing number, attached to each tire to identify each tire. The information regarding the tire identification information, the mounting position of the tire, and the axle may be stored in the storage unit 16 by an operator performing an input operation, for example, when mounting the tire on the vehicle, or by reading an RFID tag.

[0030] The groove information acquisition unit 13 acquires the tire groove data 16b from the storage unit 16 and outputs it to the wear estimation unit 15. The tire groove data 16b is data including tire identification information, new groove depth, and starting groove depth. The groove information acquisition unit 13 acquires the tire groove data 16b based on the tire identification information of the tire 7 for which wear is to be estimated. As described above, the new groove depth is the groove depth of the tire 7 in a new state, and the starting groove depth is the groove depth at the time (corresponding to the first date and time information D1) that serves as the starting point for estimating the wear state of the tire 7. The starting groove depth is assumed, for example, when estimating the wear of a tire 7 that is mounted on a vehicle and is in use. The wear situation at the starting point of wear estimation is measured to obtain the groove depth, which is used as the starting groove depth. Also, when a new tire 7 is mounted on a vehicle and wear is estimated based on the subsequent driving situation, the new groove depth and the starting groove depth will be the same value.

[0031] For the starting groove depth, it is advisable to use the latest data actually measured for the tire 7 or the value obtained by subtracting the previously estimated wear amount.

[0032] Furthermore, the tire groove data 16b includes the first date and time information D1 representing the time that serves as the starting point for estimating the wear of the tire 7, the second date and time information D2 representing the time for estimating the wear state of the tire 7, and the third date and time information D3 representing the time when the tire 7 is replaced (replacement of summer and winter tires) or the mounting position is changed. Each date and time information may include only the date or may include the time in addition to the date. When the groove information acquisition unit 13 uses each date and time information as input data to the calculation model 15a in addition to the new groove depth and the starting groove depth, it may output each date and time information included in the tire groove data 16b to the wear estimation unit 15.

[0033] The groove information management unit 14 updates the starting groove depth, the first date and time information D1, the second date and time information D2, and the third date and time information D3 associated with the tire identification information of the tire 7, and stores them in the storage unit 16 as tire groove data 16b. The groove information management unit 14 acquires the groove depth of the tire 7 measured in regular inspections or the like as the starting groove depth, and updates the tire groove data 16b. The tire wear estimation system 100 inputs data such as the running distance, speed, and acceleration after the groove depth of the tire 7 is measured into an arithmetic model 15a described later to estimate the wear state of the tire 7. The groove information management unit 14 may acquire the measured groove depth of the tire 7 as the starting groove depth and update the tire groove data 16b when changing between summer and winter tires.

[0034] When the groove information management unit 14 performs wear estimation, for example, every month, it calculates the groove depth at the time of the previous month as the starting groove depth based on the wear state estimated in the previous month. The vehicle information acquisition unit 12 calculates data such as the running distance, speed, and acceleration from the time (the first date and time information D1) when the wear state was estimated in the previous month to the time (the second date and time information D2) when wear estimation is performed as the driving situation, and outputs it to the wear estimation unit 15.

[0035] The vehicle information acquisition unit 12 acquires the driving situation using the third date and time information D3 indicating the time when the summer and winter tires are replaced. For example, consider the case where the winter tires used in the previous year's winter are also used this year. The vehicle information acquisition unit 12 reads out the vehicle measurement data 16a from the date and time (the first date and time information D1) when the groove depth (starting groove depth) of the winter tires was measured in the previous year to the date and time (the third date and time information D3) when the winter tires were replaced with summer tires, calculates the driving distance, etc., and outputs it as the driving situation to the wear estimation unit 15. Also, the vehicle information acquisition unit 12 acquires the driving situation using the third date and time information D3 indicating the time when the mounting position is changed due to tire rotation. When tire rotation is performed after the date when the starting groove depth was measured, the time series is the date and time information of the starting groove depth (the first date and time information D1), the date and time information of tire rotation (the third date and time information D3), and the date and time information for estimating the tire wear state (the second date and time information D2). Regarding tire rotation, the vehicle information acquisition unit 12 reads out, for example, the vehicle measurement data 16a from the first date and time information D1 to the third date and time information D3 at a certain mounting position M1, calculates the driving distance, etc., and outputs it as the driving situation to the wear estimation unit 15, and the wear estimation unit 15 estimates the wear state of the tire. Further, the vehicle information acquisition unit 12 reads out the vehicle measurement data 16a from the third date and time information D3 to the second date and time information D2 at the mounting position M2 after tire rotation is performed, calculates the driving distance, etc., and outputs it as the driving situation to the wear estimation unit 15, and the wear estimation unit 15 estimates the wear state of the tire.

[0036] The wear estimation unit 15 has an arithmetic model 15a and estimates the wear state of the tire 7. The arithmetic model 15a is a machine learning model that calculates the wear state (information such as wear amount and wear rate) of the tire 7 based on the input information. FIG. 3 is a schematic diagram for explaining the wear estimation and learning of the arithmetic model 15a. The input data to the arithmetic model 15a is generally classified into each system of vehicle measurement information, tire groove information, and other information.

[0037] The input data related to vehicle measurement information includes the vehicle's traveling speed, acceleration, and traveling distance. The traveling distance is acquired by the vehicle information acquisition unit 12 as described above. The input data related to tire groove information includes the new groove depth, starting groove depth of tire 7, and each date and time information (D1, D2, D3) included in the tire groove data 16b. Each date and time information is used when it is input data to the calculation model 15a in the wear estimation of tire 7. Also, when the temperature and air pressure of tire 7 are used in the wear estimation of tire 7, these pieces of information may be included in the input data.

[0038] The input data based on other information is the road surface condition estimated based on weather information, air temperature, precipitation, etc., the maximum load capacity of the vehicle included in the vehicle specification data 16c, the wear resistance performance, etc. included in the tire specification data 16d. The wear resistance performance of tire 7 uses, for example, a tire wear index value obtained by standardizing the wear resistance performance of various tread compounds with the standard compound set to 100 based on the Lamborne wear test. Also, the input data based on other information includes the mounting position of tire 7 included in the tire position data 16e, tire identification information, and information regarding the axle.

[0039] The calculation model 15a uses a learning model such as a neural network. The calculation model 15a is constructed using, for example, methods such as DNN (Deep Neural Network) and decision trees. Also, the calculation model 15a may be, for example, a multiple linear regression model for input information and generated by learning.

[0040] Figure 4 is a block diagram showing the functional configuration of the calculation model generation system 110. The calculation model generation system 110 includes, in addition to the configuration of the tire wear estimation system 100, a tire wear measurement device 60 and a calculation model generation device 20 having a learning processing unit 21, etc.

[0041] The tire wear measurement device 60 directly measures the depth of the grooves provided in the tread of the tire 7 and acquires information on the wear state of the tire 7. The operator may measure or estimate the depth of each groove using a measuring instrument, a camera, or the like, and the tire wear measurement device 60 may store the measurement data input by the operator. Further, the tire wear measurement device 60 may be a dedicated device that measures the depth of the groove by a mechanical or optical method and stores information on the wear state.

[0042] Specifically, for example, when the tire has four grooves, the tire wear measurement device 60 measures at four locations in the width direction, and further measures at three locations in the circumferential direction of the same groove, for example, at intervals of 120°. As a result, uneven wear data in the width direction or the circumferential direction of the tire is also stored in the tire wear measurement device 60. Since the diameter of the tire changes due to wear, the tire wear measurement device 60 may indirectly measure the depth of the groove by calculation from the information on the running distance and the rotation speed and speed of the tire. In addition, a device that directly measures the depth of the groove and a device that predicts by calculation from the running distance and the rotation speed and speed of the tire may be used in combination.

[0043] The calculation model generation device 20 includes a learning processing unit 21 in addition to each component of the wear estimation device 10. The parts corresponding to each component of the wear estimation device 10 in the calculation model generation device 20 have the same functions as those of the wear estimation device 10, but the calculation model 15a is before or during learning.

[0044] The learning processing unit 21 acquires information on the wear state of the tire 7 from the tire wear measurement device 60 via the communication unit 11. Referring to FIG. 3, in the learning process of the calculation model 15a, based on the input information, the wear state (wear amount, wear rate, etc.) of the tire 7 as output data is estimated by the calculation model 15a and compared with the teacher data.

[0045] The learning processing unit 21 executes learning by newly setting various coefficients in the calculation process such as weighting based on the comparison result between the wear state estimated by the calculation model 15a and the teacher data, and repeating the update of the model. The tire wear estimation system 100 estimates the wear state of the tire 7 using the learned calculation model 15a generated by the calculation model generation system 110. In the learning process of the calculation model 15a, known learning methods such as gradient boosting can be used. Also, for the verification of the calculation model 15a, known verification methods such as random data sampling and cross-validation can be used.

[0046] Next, the operations of the tire wear estimation system 100 and the calculation model generation system 110 will be described. FIG. 5 is a flowchart showing the procedure of the wear estimation process by the tire wear estimation system 100. The vehicle information acquisition unit 12 reads the vehicle measurement data 16a and acquires vehicle information such as vehicle measurement information (S1). In step S1, when the tire measurement information is used for the wear estimation of the tire 7, the vehicle information acquisition unit 12 acquires the tire measurement information stored in the storage unit 16. Also, in step S1, the vehicle information acquisition unit 12 reads out necessary information such as vehicle specifications, tire specifications, tire position, maximum load of the vehicle, and wear resistance performance of the tire from the storage unit 16 as other information. The vehicle information acquisition unit 12 reads the tire groove data 16b and calculates the travel distance based on the first date and time information D1, the second date and time information D2, and the third date and time information D3 (S2).

[0047] The groove information acquisition unit 13 reads the tire groove data 16b from the storage unit 16 and acquires the new tire groove depth, the starting groove depth, and each date and time information (S3). The wear estimation unit 15 acquires the input data from the vehicle information acquisition unit 12 and the groove information acquisition unit 13, estimates the wear state of the tire 7 using the calculation model 15a (S4), and ends the process. The calculation model 15a uses the learned calculation model generated by the calculation model generation system 110.

[0048] FIG. 6 is a flowchart showing the procedure of the generation process of the arithmetic model 15a by the arithmetic model generation system 110. The processes from step S11 to S13 shown in FIG. 6 are equivalent to the processes from step S1 to S3 shown in FIG. 5, and the description thereof is omitted for the sake of brevity. The learning processing unit 21 of the arithmetic model generation device 20 acquires information on the wear state of each tire 7 from the tire wear measurement device 60 (S14).

[0049] The wear estimation unit 15 acquires input data from the vehicle information acquisition unit 12 and the groove information acquisition unit 13, and estimates the wear state of the tire 7 by the arithmetic model 15a (S15). Incidentally, when the road surface condition or the like is used as the input data of the arithmetic model 15a, a processing unit (not shown) for estimating the road surface condition is provided, and the estimated road surface condition is input from the processing unit to the wear estimation unit 15.

[0050] The learning processing unit 21 compares the wear state of the tire 7 estimated by the arithmetic model 15a with the wear state of the tire 7 as the measured teacher data (S16). The learning processing unit 21 updates the arithmetic model 15a based on the comparison result in step S16 (S17), and ends the process. The arithmetic model generation device 20 updates the arithmetic model 15a by repeating these processes, and improves the estimation accuracy of the wear state of the tire 7.

[0051] FIG. 7 is a chart showing the accuracy of wear estimation with respect to the amount of wear. In FIG. 7, the new groove depth of tire 7 is 18 mm, and the estimation error for wear amounts from 1 mm to 12 mm is calculated. A wear amount of 1 mm means that the tire 7 has worn 1 mm from an arbitrary starting groove depth, such as a case where the groove depth of tire 7 changes from 15 mm to 14 mm, or a case where it changes from 6 mm to 5 mm. In the example, the arithmetic model 15a is learned using the new groove depth and the starting groove depth as input data according to this embodiment, and the wear state of tire 7 is estimated by the learned arithmetic model 15a using the new groove depth and the starting groove depth as input data. In the comparative example, the arithmetic model 15a is learned without using the new groove depth and the starting groove depth as input data, and the wear state of tire 7 is estimated by the learned arithmetic model 15a without using the new groove depth and the starting groove depth as input data. As shown in FIG. 7, it can be seen that the estimation error according to the example is better than that of the comparative example. Comparing the example with the comparative example, for example, the estimation error of the example at a wear amount of 6 mm is 0.11 mm better than that of the comparative example, and the estimation error of the example at a wear amount of 9 mm is 0.09 mm better than that of the comparative example. Considering that a wear amount of 0.1 mm of tire 7 corresponds to approximately a driving distance of 3000 km of the vehicle, it is considered that the estimation error is sufficiently improved in the example compared to the comparative example.

[0052] The vehicle information acquisition unit 12 of the tire wear estimation system 100 acquires information including the driving situation of the vehicle. The groove information acquisition unit 13 acquires the new groove depth, which is the groove depth of tire 7 in a new state when mounted on the vehicle, and the starting groove depth, which is the groove depth at the time point that serves as the starting point for wear estimation of tire 7. The wear estimation unit 15 estimates the wear state of tire 7 based on the machine-learned arithmetic model 15a using the driving situation vehicle information acquired by the vehicle information acquisition unit 12 and the new groove depth and the starting groove depth acquired by the groove information acquisition unit 13 as input data. Thereby, the tire wear estimation system 100 can estimate wear considering at which stage of wear progression it is, and can improve the estimation accuracy of the wear state of tire 7.

[0053] The groove information acquisition unit 13 acquires first date and time information D1 representing the point in time that is the starting point for estimating the wear of the tire 7, and second date and time information D2 representing the point in time for estimating the wear state of the tire 7. The wear estimation unit 15 inputs the driving situation of the vehicle calculated based on the first date and time information D1 and the second date and time information D2 into the calculation model 15a to estimate the wear state of the tire 7. Thereby, the tire wear estimation system 100 can estimate wear in consideration of the date and time at the starting point of wear estimation and the point in time for wear estimation, and can improve the estimation accuracy of the wear state of the tire 7.

[0054] Further, the groove information acquisition unit 13 acquires third date and time information D3 representing the point in time when the tire 7 is replaced or the mounting position is changed. The wear estimation unit 15 inputs the driving situation of the vehicle calculated based on the first date and time information D1, the second date and time information D2, and the third date and time information D3 into the calculation model 15a to estimate the wear state of the tire 7. Thereby, the tire wear estimation system 100 can estimate wear in consideration of the point in time when the tire 7 is replaced or the mounting position is changed, and can improve the estimation accuracy of the wear state of the tire 7.

[0055] In the tire wear estimation system 100, the starting groove depth may be calculated based on the wear state estimated by the wear estimation unit 15. For example, in the case where wear estimation is performed every month, the groove depth at the time of the previous month is calculated as the starting groove depth based on the wear state estimated in the previous month, and the wear state of this month is estimated. Thereby, the tire wear estimation system 100 can calculate the starting groove depth according to the period for performing wear estimation, and can estimate the wear state of the tire 7.

[0056] In the tire wear estimation system 100, the driving situation includes at least two of the driving distance, speed, and acceleration of the vehicle. Thereby, the tire wear estimation system 100 can estimate the wear state of the tire 7 in consideration of the driving situation such as the driving distance, driving speed, and acceleration of the vehicle.

[0057] The calculation model 15a is generated by machine learning using the tire wear state measured in the traveling vehicle as teacher data. Thereby, the tire wear estimation system 100 can estimate the wear state of the tire 7 by the calculation model 15a generated by machine learning.

[0058] Further, the groove information acquisition unit 13 acquires the starting groove depth for each of a plurality of grooves provided in the tire 7. The wear estimation unit 15 estimates the wear state of the tire 7 for each groove using the calculation model 15a. Thereby, the tire wear estimation system 100 can estimate the uneven wear state of the tire 7 by estimating the wear state for each of the plurality of grooves provided in the tire width direction.

[0059] The groove information management unit 14 of the tire wear estimation system 100 causes the storage unit 16 to store the starting groove depth in association with the tire identification information attached to the tire 7. The groove information acquisition unit 13 reads and acquires the starting groove depth corresponding to the tire identification information from the storage unit 16. Thereby, the tire wear estimation system 100 can manage the starting groove depth based on the tire identification information for each tire 7 and use it for estimating the wear state of the tire 7.

[0060] The tire wear estimation method of the present embodiment includes a vehicle information acquisition step, a groove information acquisition step, and a wear estimation step. The vehicle information acquisition step acquires information including the traveling state of the vehicle. The groove information acquisition step acquires the new groove depth, which is the groove depth in the new state of the tire 7 mounted on the vehicle, and the starting groove depth, which is the groove depth at the time point that serves as the starting point for estimating the wear of the tire 7. The wear estimation step estimates the wear state of the tire 7 based on the machine-learned calculation model 15a using the traveling state acquired in the vehicle information acquisition step and the new groove depth and the starting groove depth acquired in the groove information acquisition step as input data. According to this tire wear estimation method, wear can be estimated in consideration of which stage of wear progression it is in, and the estimation accuracy of the wear state of the tire 7 can be improved.

[0061] The calculation model generation system 110 includes a vehicle information acquisition unit 12, a groove information acquisition unit 13, a wear estimation unit 15, and a learning processing unit 21. The vehicle information acquisition unit 12 acquires information including the driving status of the vehicle. The groove information acquisition unit 13 acquires the new groove depth, which is the groove depth of the tire 7 in a new state when mounted on the vehicle, and the starting groove depth, which is the groove depth at the time point that serves as the starting point for estimating the wear of the tire 7. The wear estimation unit 15 estimates the wear state of the tire 7 based on the driving status acquired by the vehicle information acquisition unit 12 and the new groove depth and starting groove depth acquired by the groove information acquisition unit 13, using the learning-based calculation model 15a as input data. The learning processing unit 21 causes the calculation model 15a to learn, using, as teacher data, the wear state measured for each groove of the tire 7 that has actually traveled from the start of use to the end of wear as the vehicle equipped with the tire 7 travels on ordinary roads or highways.

[0062] FIG. 8 is a schematic diagram for explaining the definition of the positions of the grooves formed in the tire 7. The plurality of grooves formed in the tire 7 can be identified, for example, by attaching groove identification information such as a to d based on the serial surface S of the tire 7. The serial number, manufacturing symbol, etc. of the tire 7 are engraved on the serial surface, and the surface opposite to the serial surface can be identified for the tire alone. The serial surface of the tire 7 faces the inside (the center side of the axle) or the outside of the vehicle when mounted on the vehicle axle. The groove positions are identified by attaching symbols 1 to 4 in order from the outside of the vehicle. When the serial surface faces the outside, the groove identification information a, b, c, d corresponds to the groove positions 1, 2, 3, 4, respectively. When the serial surface faces the inside, the groove identification information a, b, c, d corresponds to the groove positions 4, 3, 2, 1, respectively.

[0063] The groove information management unit 14 stores in the storage unit 16 the correspondence between the groove identification information and the groove positions, including it in the tire groove data 16b. The groove information management unit 14 stores in the storage unit 16 the correspondence between the groove identification information and the groove positions after the change, including it in the tire groove data 16b, for example, when the correspondence between the groove identification information and the groove positions changes during tire rotation or tire replacement.

[0064] The groove information acquisition unit 13 may read out the correspondence relationship between the groove identification information and the groove position from the tire groove data 16b in addition to the new groove depth and the starting groove depth, etc., and output it to the wear estimation unit 15. The arithmetic model 15a may include the correspondence relationship between the groove identification information and the groove position in the input data. The wear estimation unit 15 estimates the wear state of the tire 7 using the arithmetic model 15a that includes the correspondence relationship between the groove identification information and the groove position in the input data. Thereby, the tire wear estimation system 100 can estimate the wear state of the tire 7 in consideration of the orientation when the tire 7 is mounted on the axle, and particularly can improve the estimation accuracy of the uneven wear state. Further, the arithmetic model generation system 110 can generate the arithmetic model 15a that takes into account the orientation of the tire 7 by generating the arithmetic model 15a that includes the correspondence relationship between the groove identification information and the groove position in the input data, and can improve the estimation accuracy of the uneven wear state by the arithmetic model 15a.

[0065] (Modification example) The tire groove data 16b may include elapsed information indicating the number of days elapsed from the time when the groove depth of the tire 7 was measured to the time that is the starting point of wear estimation of the tire 7. The elapsed information may be only the number of days, or may include time in addition to the number of days. The groove information acquisition unit 13 outputs the elapsed information included in the tire groove data 16b to the wear estimation unit 15 in addition to the new groove depth and the starting groove depth. The wear estimation unit 15 estimates the wear state of the tire 7 by the arithmetic model 15a based on the input data including the elapsed information.

[0066] The groove information management unit 14 calculates the number of days elapsed from the measurement date of the groove depth of the tire 7 to the time that is the starting point of wear estimation of the tire 7 as elapsed information, and updates the tire groove data 16b. The tire wear estimation system 100 can estimate the wear state of the tire 7 in consideration of the number of days elapsed from the time when the groove depth of the tire 7 was measured to the time that is the starting point of wear estimation of the tire 7 by using the elapsed information.

[0067] Generalizing the technical ideas embodied by the above embodiments and modifications, it can be said that the technical ideas described in the following items are included.

[0068] The first item is a tire wear estimation system comprising a vehicle information acquisition unit that acquires information including the driving situation of a vehicle, a groove information acquisition unit that acquires a new groove depth which is the groove depth of a tire mounted on the vehicle in a new state, and a starting groove depth which is the groove depth at the time point that is the starting point of wear estimation of the tire, and a wear estimation unit that estimates the wear state of the tire based on a machine learning-trained calculation model using the driving situation acquired by the vehicle information acquisition unit, and the new groove depth and the starting groove depth acquired by the groove information acquisition unit as input data.

[0069] The second item is the tire wear estimation system according to the first item, wherein the groove information acquisition unit acquires first date and time information representing the time point that is the starting point of wear estimation of the tire, and second date and time information representing the time point for estimating the wear state of the tire, and the wear estimation unit inputs the driving situation of the vehicle calculated based on the first date and time information and the second date and time information into the calculation model to estimate the wear state of the tire.

[0070] The third item is the tire wear estimation system according to the first item or the second item, wherein the groove information acquisition unit acquires third date and time information representing the time point when the tire is replaced or the mounting position is changed, and the wear estimation unit inputs the driving situation of the vehicle calculated based on the first date and time information, the second date and time information, and the third date and time information into the calculation model to estimate the wear state of the tire.

[0071] The fourth item is the tire wear estimation system according to any one of the first to third items, wherein the starting groove depth is calculated based on the wear state estimated by the wear estimation unit.

[0072] Item 5 is the tire wear estimation system according to any one of Items 1 to 4, wherein the driving condition includes at least two of the driving distance, speed, and acceleration of the vehicle.

[0073] Item 6 is the tire wear estimation system according to any one of Items 1 to 5, wherein the calculation model is generated by machine learning using the tire wear state measured in the traveling vehicle as teacher data.

[0074] Item 7 further includes a groove information management unit that stores the starting groove depth in a storage unit in association with the tire identification information attached to the tire, and the groove information acquisition unit reads and acquires the starting groove depth corresponding to the tire identification information from the storage unit. It is the tire wear estimation system according to any one of Items 1 to 6.

[0075] Item 8 is the tire wear estimation system according to any one of Items 1 to 7, wherein the groove information acquisition unit acquires the starting groove depth for each of a plurality of grooves provided in the tire, and the wear estimation unit estimates the wear state for each groove.

[0076] Item 9 is a tire wear estimation method including a vehicle information acquisition step of acquiring information including a driving condition of a vehicle, a groove information acquisition step of acquiring a new groove depth that is the groove depth of a tire mounted on the vehicle in a new state, and a starting groove depth that is the groove depth at the time point that is the starting point of wear estimation of the tire, and a wear estimation step of estimating the wear state of the tire based on a machine-learned calculation model using the driving condition acquired in the vehicle information acquisition step, the new groove depth, and the starting groove depth acquired in the groove information acquisition step as input data.

[0077] Item 10 is a calculation model generation system including a vehicle information acquisition unit that acquires information including a driving state of a vehicle, a groove depth information acquisition unit that acquires a new groove depth that is the groove depth of a tire mounted on the vehicle in a new state, and a starting groove depth that is the groove depth at the time point that is the starting point of wear estimation of the tire, and a wear estimation unit that estimates the wear state of the tire based on a learning type calculation model using the driving state acquired by the vehicle information acquisition unit, and the new groove depth and the starting groove depth acquired by the groove depth information acquisition unit as input data, and a learning processing unit that learns the calculation model using the wear state measured for the tire as teacher data.

[0078] As described above, the embodiments of the present invention have been described. These embodiments are examples, and it is understood by those skilled in the art that various modifications and changes are possible within the scope of the claims of the present invention, and such modifications and changes are also within the scope of the claims of the present invention. Therefore, the description and drawings in this specification should be treated as illustrative rather than restrictive.

Explanation of Reference Numerals

[0079] 7 Tire, 12 Vehicle information acquisition unit, 13 Groove depth information acquisition unit, 14 Groove depth information management unit, 15 Wear estimation unit, 15a Calculation model, 21 Learning processing unit, 100 Tire wear estimation system, 110 Calculation model generation system.

Claims

1. A vehicle information acquisition unit that acquires information including a driving status of a vehicle; a groove information acquisition unit that acquires a new groove depth, which is a groove depth of the tire when the tire is new and mounted on the vehicle, and a starting groove depth, which is a groove depth at a time point that is a starting point for estimating tire wear; a wear estimation unit that estimates a wear state of the tire based on a machine-learned calculation model using the driving conditions acquired by the vehicle information acquisition unit, and the new groove depth and the starting groove depth acquired by the groove information acquisition unit as input data; and A tire wear estimation system comprising:

2. The groove information acquisition unit acquires first date and time information representing a time point that is a starting point for estimating the wear of the tire, and second date and time information representing a time point at which the wear state of the tire is estimated, The tire wear estimation system according to claim 1 , wherein the wear estimation unit estimates the wear state of the tire by inputting the vehicle driving conditions calculated based on the first date and time information and the second date and time information into the calculation model.

3. The groove information acquisition unit acquires third date and time information representing a time point when the tire was replaced or the mounting position of the tire was changed, The tire wear estimation system of claim 2, wherein the wear estimation unit inputs the vehicle's driving conditions, calculated based on the first date and time information, the second date and time information, and the third date and time information, into the calculation model to estimate the wear state of the tire.

4. The tire wear estimation system according to claim 1 , wherein the starting groove depth is calculated based on the wear state estimated by the wear estimation unit.

5. The tire wear estimation system according to claim 1 , wherein the driving conditions include at least two of a travel distance, a speed, and an acceleration of the vehicle.

6. The tire wear estimation system according to claim 1 , wherein the calculation model is generated by machine learning using the tire wear state measured on a traveling vehicle as training data.

7. A groove information management unit stores the starting groove depth in a storage unit in association with tire identification information attached to the tire, The tire wear estimation system according to claim 1 , wherein the groove information acquisition unit reads and acquires the start groove depth corresponding to the tire identification information from the storage unit.

8. The groove information acquisition unit acquires the starting groove depth for each of a plurality of grooves provided in the tire, The tire wear estimation system according to claim 1 , wherein the wear estimation unit estimates a wear state for each of the grooves.

9. A vehicle information acquisition step of acquiring information including a driving status of the vehicle; a groove information acquisition step of acquiring a new groove depth, which is a groove depth of the tire in a new state of the tire mounted on the vehicle, and a starting groove depth, which is a groove depth at a time point that is a starting point for estimating tire wear; a wear estimation step of estimating a wear state of the tire based on a machine-learned calculation model using as input data the driving conditions acquired in the vehicle information acquisition step, and the new tire groove depth and the starting groove depth acquired in the groove information acquisition step; A tire wear estimation method comprising:

10. A vehicle information acquisition unit that acquires information including a driving status of a vehicle; a groove information acquisition unit that acquires a new groove depth, which is a groove depth of the tire when the tire is new and mounted on the vehicle, and a starting groove depth, which is a groove depth at a time point that is a starting point for estimating tire wear; a wear estimation unit that estimates a wear state of the tire based on a learning-type calculation model in which the driving conditions acquired by the vehicle information acquisition unit, and the new tire groove depth and the starting groove depth acquired by the groove information acquisition unit are used as input data; a learning processing unit that learns the calculation model using a wear state measured for the tire as training data; A computational model generation system comprising:

Images